1. Field of The Invention
The present invention relates to an apparatus and method for automatically generating membership functions of input variable(s) and output variable and/or their applied fuzzy rules for a fuzzy inference system.
2. Description of The Background Art
A previously proposed fuzzy inference system is exemplified by a U.S. patent application Ser. No. 07/581,770 filed on Sep. 13, 1990 having a priority based on a Japanese Patent Application No. Heisei 1-239514 which corresponds to an R. O. C. (Taiwan) Patent Application No. 79107756 filed on Sep. 14, 1990 (which is now patented as Patent No. 47856 issued an Oct. 1, 1991), and which corresponds to a Korean Patent Application No. 90-14455 filed on Sep. 13, 1991.
As appreciated from the disclosed fuzzy inference system described above, it is most important to carry out full tuning operations including collections of data on know-hows of well skilled experts and their simulations when an architecture of a control system is designed.
FIG. 1 shows a procedure of designing the architecture of the expert fuzzy control system.
In FIG. 1, a step S1 is a step for understanding a system to be subjected to the system architecture for a designer.
That is to say, in the step S1, the expert investigates and specifically understands the system what can be measured or cannot be measured, what kind PG,3 of operations the expert should made, what is an object of providing the system, and what is a problem to be solved by the system.
In a step S2, the data on know-hows of a well skilled operator are collected. That is to say, in the step S2, when the well skilled expert drives the system to be controlled, data of the know-hows such as in which states what the system notices, what objects lies in the system, what kind of operation the system is required, how the system is operated, and so on.
In such a collecting operation as described above, it should be paid to attention that states of process are roughly classified into three or five levels (high, intermediate, and low), their operations are rather finely classified into five or seven levels (considerably small, small, intermediate, rather large, considerably large), and, then, the know-hows are obtained through interviews and/or enquire (=poll) to the well skilled operators. how many levels the classification can be easily be made are, generally, five levels. This classification is called a fuzzy clustering. Then, these know-hows are evolved into linguistic rules of IF.about.THEN forms.
Next, the membership functions are extracted.
Generally, various types of membership functions are used such as triangle type, bell shaped type, some function type, and trapezoid type. The best expressible type from among these types of the membership function is said to be an exponential function type. (However, actually used type is the triangular type.) Then, in this case, only a vertex of the triangular membership function expressing each fuzzy level may be collected from the well skilled operators. One of the reasons is that what is expressed in the linguistic fuzzy rule is a characterized point and the fuzzy inference is carried out through an inner interpolation and the other reason is that a clear answer cannot be obtained for a question, e.g., of which value corresponds to about 0.8 or rather intermediate.
A step S3 is a step for collecting operation data. This step S3, that is to say, is to collect values of ruled variables in the step S2 using a data recorder (for example, a temperature, pressure, or flow quantity as an input variable and an opening angle of a valve as an output variable). Then, a matching is taken on the basis of the data with the operating know-hows derived in the step S2.
In a step S4, the know-hows are applied to a fuzzy controller of the fuzzy interference system. That is to say, the step S4 teaches definitions of variables carried out when the operating know-hows of the well skilled expert determined in the step S3 are applied to the control system. The definitions of the variables are such that attributes of the variables handled in IF.about.THEN.about.linguistic rules. Thereafter, fuzzy levels and membership functions of the respective variables are input to the fuzzy controller and the fuzzy control linguistic rules are registered.
In a step S5, the simulation is carried out. In details, the step S5 is a step for simulating using the control rules and membership functions input in the step S4. Then, the data collected in the step S3 are used as evaluation functions and a fuzzy modeling of the system to be controlled is carried out.
The control for the modeling is carried out and simulated. In addition, the recorder is used to collect and analyze operations of the well skilled expert and controlled variables and their directions derived from the fuzzy controller.
In a step S6, an actual operation of the fuzzy inference system is carried out. After a preparation of the series of steps S1 through S5 is carried out, the actual operation thereof is carried out. In the step S6, minute tunings of the membership functions are repeated to achieve an optimization of the fuzzy interference system.
The step S2 for collecting the data on the know-hows from the well skilled operators is an important factor to determine whether the fuzzy control system becomes put into practice from among the steps S1 through S6 of FIG. 1.
Although the rules and membership functions can to some degree be determined according to the step S2, the step S2 is not always appropriate for determining them. The reason is that problems of how well the know-hows of the well skilled experts can be fuzzy clustered and to what degree the know-hows can be expressed as the fuzzy rules are not clearly defined.
In addition, since the fuzzy labels representing the membership functions are uniformly generated, such fuzzy expressions as large and/or rather large cannot be made.